As society continues to develop data manipulation techniques, immediate verification of data has become possible. In some instances, verification includes cloud-based verification of information. This cloud-based data verification occurs via the “cleansing” of supplied data. Cleansing data may include validating received data and/or supplying missing information to provide a more complete representation of the data. For example, websites sometimes cleanse address data provided by users to obtain more accurate address representations.
Even with the cleansing of addresses via data validation or supplementation, however, uncertainties may occur regarding the real-world utilization of the cleansed address. For example, postal authorities may maintain comprehensive reference address information in urban areas but may maintain a lower level of reference address accuracy in rural areas. Further, in some countries, postal authority data may not be commercially available, may be so poor that the data may not be a credible source of information, or may not exist. When used to cleanse addresses, this poor postal authority reference data may cause the cleansed address to become useless when utilized. In a delivery context, even if an address is cleansed, if the cleansed address contains errors due to poor reference data, the utilization of the cleansed address may result in a failed delivery.
In the drawings, like reference numbers generally indicate identical or similar elements. Additionally, generally, the left-most digit(s) of a reference number identifies the drawing in which the reference number first appears